Contents
Chapter 1: Overview and Introduction
1.2 Issues of Multispectral and Hyperspectral Imageries
1.3 Divergence of Hyperspectral Imagery from Multispectral Imagery
1.6 Laboratory Data to be Used in This Book
1.7 Real Hyperspectral Images to be Used in this Book
1.8 Notations and Terminologies to be Used in this Book
Chapter 2: Fundamentals of Subsample and Mixed Sample Analyses
2.4 Kernel-Based Classification
Chapter 3: Three-Dimensional Receiver Operating Characteristics (3D ROC) Analysis
3.2 Neyman–Pearson Detection Problem Formulation
3.5 Real Data-Based ROC Analysis
Chapter 4: Design of Synthetic Image Experiments
4.2 Simulation of Targets of Interest
4.3 Six Scenarios of Synthetic Images
Chapter 5: Virtual Dimensionality of Hyperspectral Data
5.3 VD Determined by Data Characterization-Driven Criteria
5.4 VD Determined by Data Representation-Driven Criteria
5.5 Synthetic Image Experiments
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